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Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

2021; Multidisciplinary Digital Publishing Institute; Volume: 9; Issue: 16 Linguagem: Inglês

10.3390/math9161839

2227-7390

Broderick Crawford, Ricardo Soto, José Lemus-Romani, Marcelo Becerra, Jose M. Lanza-Gutiérrez, Nuria Caballé, Mauricio Castillo, Diego Tapia, Felipe Cisternas-Caneo, José García, Gino Astorga, Carlos Castro, José-Miguel Rubio,

Scheduling and Timetabling Solutions

One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.